Title: Characterising uncertainties in human exposure modelling through the Random Sampling-High Dimensional Model Representation (RS-HDMR) methodology

Authors: Sheng-Wei Wang, Panos G. Georgopoulos, Genyuan Li, Herschel Rabitz

Addresses: Environmental and Occupational Health Sciences Institute, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA. ' Environmental and Occupational Health Sciences Institute, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA. ' Department of Chemistry, Princeton University, Princeton, NJ 08544, USA. ' Department of Chemistry, Princeton University, Princeton, NJ 08544, USA

Abstract: This paper presents the application of a quantitative model assessment and analysis tool, the Random Sampling-High Dimensional Model Representation (RS-HDMR), in characterising uncertainties of population-based human exposure modelling to trichloroethylene (TCE). The RS-HDMR method is used to construct the Fully Equivalent Operational Model (FEOM) as an ||accurate and efficient|| approximation of the mechanistic multimedia and multipathway exposure and dose model for calculating internal doses of TCE, so as to reduce ||model uncertainty|| that may result from simplifying approximations (e.g. steady-state assumptions) of the original mechanistic model in order to obtain computational efficiency. RS-HDMR can also be used to assess the influence of ||parameter uncertainty|| on model outputs by providing quantitative estimates and qualitative descriptors of independent and cooperative influences of model parameters/inputs on the output through global uncertainty/sensitivity analysis. The outcomes of global uncertainty/sensitivity analysis can be used to direct available resources towards reducing uncertainty where it is most appropriate.

Keywords: global uncertainty; sensitivity analysis; HDMR; human exposure assessment; risk assessment; microenvironmental models; model reduction; Monte Carlo methods; multimedia exposure; multipathway exposure; human exposure modelling; dose modelling; pharmacokinetics models; random sampling; trichloroethylene; model uncertainty; environmental contaminants; health risks.

DOI: 10.1504/IJRAM.2005.007179

International Journal of Risk Assessment and Management, 2005 Vol.5 No.2/3/4, pp.387 - 406

Published online: 02 Jun 2005 *

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